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Current diagnostic approaches of AD, such as neuroimaging, cognitive assessments and biomarker research, are lengthy, vague and not sufficient to assess the early stages of AD. To address these problems, we introduced a novel deep learning model, \u2018NeuroMixFormer\u2019, which is based on a mixture\u2010of\u2010experts architecture for AD classification from MRI. The proposed multistage architecture employs a dynamic routing mechanism and four expert blocks per stage, each integrating dense connectivity with a spatial and channel attention module for feature extraction. To improve early feature learning, auxiliary classifiers are incorporated at intermediate stages of training. Evaluations on three datasets (ADNI, Mendeley and Kaggle Augmented Alzheimer's MRI) demonstrated the proposed model's superior performance over existing deep learning architectures and state\u2010of\u2010the\u2010art methods, achieving up to 99.48% accuracy on the Kaggle dataset, 90.28% on the Mendeley dataset and 99.86% on the ADNI dataset, respectively. Ablation studies confirmed the importance of dual\u2010attention mechanisms, and expert routing analysis showed clear specialisation patterns across AD stages, improving both classification accuracy and interpretability. These results underscore the effectiveness and generalisability of NeuroMixFormer in automated dementia detection, highlighting its potential to support early and precise AD diagnosis. However, the high computational cost and inference time associated with this high accuracy limit the practicality of the proposed approach in clinical settings.<\/jats:p>","DOI":"10.1111\/exsy.70223","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T22:55:04Z","timestamp":1770764104000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Multistage Attention\u2010Enhanced Mixture of Experts Model for Alzheimer's Disease Diagnosis"],"prefix":"10.1111","volume":"43","author":[{"given":"Muhammad John","family":"Abbas","sequence":"first","affiliation":[{"name":"Center of Artificial Intelligence, Prince Mohammad Bin Fahd University  Al\u2010Khobar Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2023-6980","authenticated-orcid":false,"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[{"name":"Center of Artificial Intelligence, Prince Mohammad Bin Fahd University  Al\u2010Khobar Saudi Arabia"}]},{"given":"Veena","family":"Dillshad","sequence":"additional","affiliation":[{"name":"Department of Computer Science HITEC University  Taxila Pakistan"}]},{"given":"Ahmed Ibrahim","family":"Alzahrani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering College of Applied Studies, King Saud University  Riyadh Saudi Arabia"}]},{"given":"Nasser","family":"Alalwan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering College of Applied Studies, King Saud University  Riyadh Saudi Arabia"}]},{"given":"Ali","family":"Alamer","sequence":"additional","affiliation":[{"name":"Department of Radiology College of Medicine, Qassim University  Buraydah Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3318-9394","authenticated-orcid":false,"given":"Yunyoung","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Soonchunhyang University  Asan Republic of Korea"}]},{"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[{"name":"Nuffield Department of Primary Care Health Sciences University of Oxford  Oxford UK"},{"name":"School of Computing, Edinburgh Napier University  Edinburgh UK"}]}],"member":"311","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"e_1_2_13_2_1","unstructured":"A. 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